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András Bárdossy IWS Universität Stuttgart
Copulas (2) András Bárdossy IWS Universität Stuttgart
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Sampling only at a number of locations What is between ?
Spatial problems Sampling only at a number of locations What is between ? Estimate Quality of estimation Simulate realizations Geostatistics (Krige, Matheron) Mining applications Hydro and Environmental sciences
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Geostatistics Z(x) Random function – Realisation z(xi) Assumption – „uniform continuity“ No differences are known a-priori Independent of the location – depends only on h (Semi)Variogramm Covariance function
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Experimental Variogramm EC
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Point kriging Unbiasedness
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Estimation variance using the variogram
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Kriging equations using variogram
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Estimation variance is an index of spatial configuration
Problems Estimation variance is an index of spatial configuration Does not depend on the local values “Best” for Gaussian distribution Symmetrical (high and low values not distinguished) Variogram estimation difficult Squared differences – skewed distribution Dominated by high values Independence of the pairs not fulfilled Strongly influenced by the marginal distribution
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Digital elevation models – water dominated regions Contaminations
Symmetry Digital elevation models – water dominated regions Maxima and minima Contaminations Source vs Background concentrations Known but unquantified deterministic processes lead to asymmetry and non-Gaussian dependence
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Indicator Variables Indicator variables Indicator variogram
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Indicator variables Interpretation as probability Interpolation of the indicators Result pdf for each location Simulation restricted to the observed range Can copulas be used to overcome some of these problems?
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How to find such copulas ?
Spatial copulas Assumption: Multivariate copula exists for any number of points The bi-variate marginal copulas corresponding to pairs separated by a vector h are translation invariant How to find such copulas ?
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Empirical copulas Set of pdf pairs corresponding to points separated by the vector h Generalization of the variogram Empirical density using kernel smoothing
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Empirical copula density chloride h=5000m
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Empirical copula density chloride h=30000m
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Empirical copula density nitrate h=5000m
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Empirical copula density pH h=5000m
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Cl Variogramm
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pH Variogramm
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Conditional Entropy: Nitrate 3000 m and 30000m
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Copulas and natural processes
Natural processes influence high and low values differently Erosion at high elevations Pollution is spreading not the background Weather relates the high discharges Copulas of digital elevation models: Spain – eroded old landscape Ecuador – younger but erroded Mars – eroded and meteorites
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Copula density of the pair C8 and C9
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Copulas of daily rainfall
601 rainfall stations in the Rhein catchment Germany Size = km2 Days with important events with good spatial coverage were selected (400 days of the period ) Spatial copulas (densities) for different distances were calculated
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Spatial dependence – 5 km Event 70
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Spatial dependence – 5 km Event 347
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Spatial dependence – 5 km Event 159
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Radarniederschlag 29. Dezember 2001 11:20-13:20
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Copula Radarniederschlag 29. Dezember 2001 11:20-13:20
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Requirements for a spatial copula
Stability of the multivariate marginals: which means that any multivariate marginal copula corresponding to a selected set of points should not depend on the set of other selected points used to define the multivariate copula. Wide range of dependence: a geographically close set of points should have an arbitrarily strong dependence structure, while distant points should be independent. Flexible parametrization: the multivariate copula should have a parametrization such that the dependence structure reflects the geometric position of the corresponding set of points.
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Definition of a copula from a multivariate distribution:
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Multivariate normal copula Derived multivariate copulas
Possibilities Multivariate normal copula Simple but symmetrical Derived multivariate copulas If g monotonic – no change of the copula If g non monotonic one can get interesting copulas
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Normal copula Correlation = 0.85
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Chi square Non central chi-square distribution
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Chi square Multivariate case
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n-dimensional Chi-square copula
Density
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Chi-Square Copulas
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Gauss – Chi-square
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V-transformed copula Transformation function: Strong dependence of the extremes if shifted to one side and partly to the middle If k=1 then it is the chi square copula
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K=2, m=1
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Empirical copula density chloride h=5000m
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Parameter estimation Non independent pairs – ML Fit the rank correlation function and the asymmetry Parametric form of the covariance of the original normal Further work needed
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For n+1 points the joint distribution is known
Interpolation For n+1 points the joint distribution is known Calculate the conditional for the unobserved point Full conditional distribution known – thus confidence intervals can be calculated Example: 4 points – corner of a unit square A: two of them with F(x)=1 two with F(x)=0 B: all with F(x)=0.5
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Example interpolation – conditional densities m=0, k=1
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Example interpolation – conditional densities m=1, k=3
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Example interpolation – conditional densities normal copula
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Validation of the conditional densities
Are the conditional densities OK ? Cross validation Calculation of the frequencies of non exceedence for the observed values Comparison with the uniform V is much better then normal or Kriging
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Thank you ! bardossy@iws.uni-stuttgart.de
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Nitrat und Phosphat
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